The development of hardware neural networks, including neuromorphic hardware, has been
accelerated over the past few years. However, it is challenging to operate very large-scale …

We present here the design and applications of an arousal-based model controlling the
behavior of a Sony AIBO robot during the exploration of a novel environment: a children's …

In what ways can artificial life contribute to the scientific exploration of cognitive, affective
and social processes? In what sense can synthetic models be relevant for the advancement …

In the context of our work in developmental robotics regarding robot–human caregiver
interactions, in this paper we investigate how a “baby” robot that explores and learns novel …

Many real-world networks are directed, sparse, and hierarchical, with a mixture of
feedforward and feedback connections with respect to the hierarchy. Moreover, a small …

Models of associative memory usually have full connectivity or, if diluted, random symmetric
connectivity. In contrast, biological neural systems have predominantly local, non-symmetric …

Small-world neural networks, as well as diluted Hopfield networks, are constructed by using
matrix decomposition and a connection elimination strategy. It is shown that, to a certain …

Models of associative memory usually have full connectivity or if diluted, random symmetric
connectivity. In contrast biological neural systems have predominantly local, non-symmetric …

Driven by growing computational power and algorithmic developments, machine learning
methods have become valuable tools for analyzing vast amounts of data. Simultaneously …

This work presents an approach to optimize the weights of a discrete Hopfield network as
mixed integer linear program (MILP). As the original formulation involves a sign-function, it is …